scholarly journals Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Zhang ◽  
Xinyuan Chen
Keyword(s):  
Signal Reconstruction ◽  
Pearson Correlation ◽  
Hydraulic Cylinder ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Systems ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Ae Signals

Internal leakage diagnosis in a hydraulic cylinder is a key technique for the maintenance of hydraulic systems. However, it is difficult to diagnose the internal leakage under different low loads. To solve this problem, a novel fault diagnosis method based on the optimization deep belief network (DBN) combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is proposed to treat the collected AE signals. The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. The reconstructed signals are regarded as the input of the optimization DBN, and the particle swarm optimization simulated annealing (PSOSA) algorithm is adopted to identify the four internal leakage levels. The experimental results show that the proposed method exhibits a higher classification accuracy than other methods under different low loads. This result validates the effectiveness and superiority of the proposed approach to realize internal leakage diagnoses under different low loads.

scholarly journals A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1039 ◽  
Author(s):  
Haikun Shang ◽  
Yucai Li ◽  
Junyan Xu ◽  
Bing Qi ◽  
Jinliang Yin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Partial Discharge ◽  
Hybrid Approach ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Fusion Algorithm ◽  
Mode Decomposition ◽  
Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

Internal leakage identification of hydraulic cylinder based on intrinsic mode functions with random forest

2019 ◽  
Vol 233 (15) ◽  
pp. 5532-5544 ◽  
Author(s):  
Lin Li ◽  
Yixiang Huang ◽  
Jianfeng Tao ◽  
Chengliang Liu
Keyword(s):  
Random Forest ◽  
Hydraulic Cylinder ◽  
Support Vector ◽  
Hydraulic Systems ◽  
Intrinsic Mode Functions ◽  
Random Forest Algorithm ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Hydraulic Cylinders ◽  
Experimental Pressure

Monitoring for internal leakage of hydraulic cylinders is vital to maintain the efficiency and safety of hydraulic systems. An intelligent classifier is proposed to automatically evaluate internal leakage levels based on the newly extracted features and random forest algorithm. The inlet and outlet pressures as well as the pressure differences of two chambers are chosen as the monitoring parameters for leakage identification. The empirical mode decomposition method is used to decompose the raw pressure signals into a series of intrinsic mode functions to obtain the essence in experimental signals. Then, the features extracted from intrinsic mode functions in terms of statistical analysis are formed the input vector to train the leakage detector. The classifier based on random forest is established to categorize internal leakage into proper levels. The accuracy of the internal leakage evaluator is verified by the experimental pressure signals. Moreover, an internal leakage evaluator is established based on the support vector machine algorithm, in which the wavelet transform is applied for feature extraction. The accuracy and efficiency of different classifiers are compared based on leakage experiments. The results show that the classifier trained by the intrinsic mode function features in terms of random forest algorithm may more effectively and accurately identify internal leakage levels of hydraulic cylinders. The leakage evaluator provides probability for online monitoring of the internal leakage of hydraulic cylinders based on the inherent sensors.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

scholarly journals A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Chenguang Huang ◽  
Jianhui Lin ◽  
Jianming Ding ◽  
Yan Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Values ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Periodic Impulse ◽  
Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

A fault diagnosis method of rolling element bearings based on CEEMDAN

2015 ◽  
Vol 231 (10) ◽  
pp. 1804-1815 ◽  
Author(s):  
Yaguo Lei ◽  
Zongyao Liu ◽  
Julien Ouazri ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Noisy Signals ◽  
Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

2013 ◽  
Vol 347-350 ◽  
pp. 426-429 ◽  
Author(s):  
Wen Bin Zhang ◽  
Yan Jie Zhou ◽  
Jia Xing Zhu ◽  
Ya Song Pu
Keyword(s):  
Fault Diagnosis ◽  
Rank Order ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Sampled Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Grey Relation

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

scholarly journals Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Peng-fei Zheng ◽  
Jun Wang
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Coupling System ◽  
Reconstructed Signal ◽  
Gear Transmission System ◽  
Gear Fault ◽  
Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

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